Measuring Gene Expression in Regenerating and Cancerous Kidney: from Population to the Single Cell Level
Abstract: The human body consists of approximately 1015 different cells. Although all these cells share almost identical DNA content, they present a wide range of phenotypic characteristics. Each cell is characterized - for the most part - by a different set of genes that it is expressing which determine its structure and function. A major challenge of contemporary biomedicine is to identify genes responsible for complex disease such as cancer in order to design drugs that will specifically target them. Although live and fixed cells can be visualized at high resolution under a microscope, it is hard to quantitatively characterize genome-wide cellular processes such as the response of a tumor to an anti-cancer drug. Moreover, it is almost impossible to identify rare cell populations inside tumors that are responsible for tumor regeneration, relapse, and metastasis – cells also known as “cancer stem cells”.
To overcome these limitations, genomic technologies such as microarrays and RNA-sequencing were developed in order to measure the expression levels of thousands of genes simultaneously. This has paved the way to a better understanding of the gene circuits that regulate tissue regeneration and how they are distorted in cancer. Recent technological advances have down- scaled theses genomic technologies to the single cell level. Thus not only the average “typical cell” can be characterized but rather the whole repertoire of different cell subpopulations that co-exist within the sample. This is important since characterization of the “cancer stem cell” sub-populations can help design drugs to specifically target them.
In my talk I will show how we can use RNA sequencing technology to identify gene sets that are up-regulated in kidney tumors that were treated with a drug, as well as in populations of putative cancer stem cells. In addition I will present a computational method to identify significant cell sub-populations from single cell gene expression datasets. Finally, I will discuss my plan to use RNA sequencing with molecular barcoding to count mRNA transcripts from 150-200 genes in hundreds, or even thousands, of individual cells.